Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M. and Bouchachia, A., 2014. A Survey on Concept Drift Adaptation. ACM Computing Surveys, 46 (4), p. 44.
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Concept drift primarily refers to an online supervised learning scenario when the relation between the in- put data and the target variable changes over time. Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the concept drift adaptation presents the state of the art techniques and a collection of benchmarks for re- searchers, industry analysts and practitioners. The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.
|Uncontrolled Keywords:||concept drift, change detection, adaptive learning|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||23 Sep 2015 14:27|
|Last Modified:||06 Jan 2016 16:59|
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